✳flâneur — a map of the web's best reading

Scaling RAG from POC to Production | by Anurag Bhagat | Oct, 2024 | Towards Data Science

towardsdatascience.com · 1,895 words · saved by 1 readers

Those of you who have been immersed in generative AI and its large-scale applications outside of personal productivity apps have likely come across the notion of Retrieval Augmented Generation or RAG. The RAG architecture consists of two key components—the retrieval component which uses vector databases to do an index based search on a large corpus of documents. This is then sent over to a large language model (LLM) to generate a grounded response based on the richer context in the prompt. Whether you are building customer-facing chatbots to answer repetitive questions and reduce workload from customer service agents, or building a co-pilot for engineers to help them navigate complex user manuals step-by-step, RAG has become a key archetype of the application of LLMs. This has enabled LLMs to provide a contextually relevant response based on ground truth of hundreds or millions of documents, reducing hallucinations and improving the reliability of LLM-based applications. If you are ask

Scaling RAG from POC to Production | Towards Data Science ChatGPT Scaling RAG from POC to Production Common challenges and architectural components to enable scaling Anurag Bhagat Oct 7, 2024 8 min read Share Source: Generated with the help of AI (OpenAI’s Dall-E model) 1. Introduction 1.1. Overview of RAG Those of you who have been immersed in generative AI and its large-scale applications outside of personal productivity apps have likely come across the notion of Retrieval Augmented Generation or RAG. The RAG architecture consists of two key components—the retrieval component which use

Explore this link on the map →

saved by

related reading